Microbial Ecology

The Bacterial Communities of Full-Scale Biologically Active, Granular Activated Carbon Filters Are Stable and Diverse and Potentially Contain Novel Ammonia-Oxidizing Microorganisms

Timothy M. LaPara, Katheryn Hope Wilkinson, Jacqueline M. Strait, Raymond M. Hozalski, Michael J. Sadowksy, Matthew J. Hamilton
K. E. Wommack, Editor
DOI: 10.1128/AEM.01692-15

ABSTRACT

The bacterial community composition of the full-scale biologically active, granular activated carbon (BAC) filters operated at the St. Paul Regional Water Services (SPRWS) was investigated using Illumina MiSeq analysis of PCR-amplified 16S rRNA gene fragments. These bacterial communities were consistently diverse (Shannon index, >4.4; richness estimates, >1,500 unique operational taxonomic units [OTUs]) throughout the duration of the 12-month study period. In addition, only modest shifts in the quantities of individual bacterial populations were observed; of the 15 most prominent OTUs, the most highly variable population (a Variovorax sp.) modulated less than 13-fold over time and less than 8-fold from filter to filter. The most prominent population in the profiles was a Nitrospira sp., representing 13 to 21% of the community. Interestingly, very few of the known ammonia-oxidizing bacteria (AOB; <0.07%) and no ammonia-oxidizing Archaea were detected in the profiles. Quantitative PCR of amoA genes, however, suggested that AOB were prominent in the bacterial communities (amoA/16S rRNA gene ratio, 1 to 10%). We conclude, therefore, that the BAC filters at the SPRWS potentially contained significant numbers of unidentified and novel ammonia-oxidizing microorganisms that possess amoA genes similar to those of previously described AOB.

INTRODUCTION

Public water utilities use surface water to produce high-quality, potable drinking water for almost 70% of the people residing in the United States (1). The conventional process for treating surface water involves a series of unit operations that include coagulation, flocculation, sedimentation, filtration, and disinfection (2). The application of these technologies has generally proven effective for protecting public health (2), although numerous exceptions have been reported (3). Given the presumptive safety of public water supplies, water consumers are increasingly concerned about the taste and odor of public water supplies as well as other esthetic concerns (4).

Public water utilities, therefore, have begun to augment their treatment processes to specifically remove taste- and odor-causing compounds, such as geosmin and 2-methylisoborneol (5–7). Although a few alternative technologies can be used, filtration using biologically active, granular activated carbon (BAC) has proven successful because of its simplicity (i.e., it is easily retrofitted into preexisting operations) and effectiveness (8, 9). Filtration with BAC differs from filtration with conventional granular media (e.g., sand and anthracite) because of its high sorptive capacity (6, 8) as well as the active biofilm that grows on its surface (10–13). The biofilm on the BAC filter medium provides direct biodegradation of dissolved geosmin as well as biological regeneration of the filter medium by metabolizing the initially sorbed geosmin as it desorbs from the carbon (8, 14).

In this study, we investigated the bacterial community dynamics of the full-scale BAC filters at the Saint Paul Regional Water Surfaces (SPRWS) in St. Paul, MN. The goal of this study was to understand the bacterial community structure of these full-scale BAC filters with respect to filter-to-filter variability as well as their associated seasonal dynamics. Bacterial community composition was tracked using Illumina sequencing of the V6 region of PCR-amplified 16S rRNA genes. These results demonstrated that the bacterial communities growing on the BAC filters were highly diverse but maintained a few keystone populations among all filters and throughout the year. The most common phylotype was from the genus Nitrospira, which is generally assumed to be a chemolithotrophic organism that oxidizes nitrite. Curiously, very few of the known ammonia-oxidizing bacteria (AOB; that is, of the order Nitrosomonadales) were found in the Illumina profiles. Subsequent quantitative real-time PCR (qPCR) analyses, however, detected substantial quantities of amoA genes more consistent with the fraction of Nitrospira-like populations in the Illumina profiles. This disparity between ratios of amoA to 16S rRNA genes detected by qPCR and the fraction of known AOB in the Illumina profiles suggests that these bacterial communities harbor previously unidentified ammonia-oxidizing microorganisms. We speculate that the predominance of AOB and nitrite-oxidizing bacteria (NOB) in these BAC filters was driven by the practice of backwashing the filters with chloraminated finished water that could liberate 0.5 to 1.0 mg/liter of ammonia (as N) upon reaction with the granular activated carbon (GAC) medium (15).

MATERIALS AND METHODS

SPRWS process description.SPRWS provides drinking water to almost 420,000 people residing in the city of Saint Paul, Minnesota, and several nearby communities. The facility has the capacity to treat 144 million gallons per day, although it currently produces an average of 45 million gallons per day. The Mississippi River is the primary source of water for SPRWS, although the river water passes through three different lakes (Lake Pleasant, Sucker Lake, and Lake Vadnais) before it enters the facility. The treatment process consists of a series of unit operations, including coagulation (aluminum sulfate), softening (lime addition), flocculation, sedimentation, filtration, and disinfection (chloramination). The filtration system includes 24 individual filter beds, each containing ∼1 m of GAC on top of 10 cm of sand. Each filter bed was backwashed once per week to remove particles that had accumulated over time on the GAC medium. The backwashing process began with an air scour (flow rate, 25 m3/h), following by forcing water through the filters at rates of 5.5 to 13.8 gallons per minute per square foot.

Water samples for chemical analysis were collected by SPRWS personnel as part of routine monitoring. Raw water samples were collected at the inlet to the treatment facility; finished water samples were collected from the clear well, just prior to the water entering the distribution system. All chemical analyses in this study were performed by SPRWS personnel according to EPA method 350.1 (ammonia), EPA method 353.2 (nitrite/nitrate), and standard method 5310B (total organic carbon [TOC]) (16). The method used for ammonia analysis also quantifies the nitrogen contained within chloramine.

BAC filter sample collection and preparation.Samples were collected from the top layer of the BAC filters using clean and disinfected 1-liter bottles and immediately transported to the University of Minnesota (<4 h). Triplicate subsamples (∼0.5 g [wet weight]) were immersed in lysis buffer (5% SDS, 10 mM NaPO4, pH 8) and then rigorously shaken for 30 s in a FastPrep machine (MP Biomedical, Solon, OH). Genomic DNA was extracted using the Spin Kit for Soil (MP Biomedical) according to the manufacturer's directions. Genomic DNA was stored at −20°C until needed.

PCR and Illumina MiSeq analysis.Triplicate PCRs targeting the V6 region of the 16S rRNA were performed on each DNA extract as described previously (17). Briefly, the PCR primers were a mixture of five modified 967F primers known to amplify both Bacteria and Archaea (18) coupled to the 1046R primer with a six-base multiplexing identification barcode attached to the 5′ end (19). PCR products were initially screened using 2% agarose gels and purified using the QIAquick gel extraction kit (Qiagen, Valencia, CA) per the manufacturer's instructions. Purified PCR products were pooled in equal concentrations and used as the template for paired-end sequence analysis (2 by 150 bp) on a MiSeq desktop sequencer (Illumina, Inc., San Diego, CA) at the University of Minnesota Genomics Center (UMGC). PCR products were pooled into four sets of 20 individual samples; library preparation and sequencing were done according to the manufacturer's instructions.

qPCR.Quantitative real-time PCR (qPCR) was conducted using an Eppendorf Mastercycler EP Realplex thermal cycler (Eppendorf, Westbury, NY) to quantify 16S rRNA genes (a measure of total bacterial biomass) (20) and two different clades of amoA genes (specific to ammonia-oxidizing bacteria and ammonia-oxidizing Archaea, respectively) (21, 22). Each qPCR run consisted of initial denaturation for 10 min at 95°C, followed by 40 cycles of denaturation at 95°C for 15 s, and annealing/extension at a target-specific temperature for 1 min. A 25-μl reaction mixture contained 12.5 μl of iTaq SYBR green Supermix with 6-carboxyl-X-rhodamine (ROX) (Bio-Rad, Hercules, CA), 25 μg bovine serum albumin (Roche Applied Science, Indianapolis, IN), optimized quantities of forward and reverse primers, and a specified volume of template DNA (usually 0.5 μl). The precise volume and concentration of template DNA were empirically optimized for each sample to generate the lowest detection limit while minimizing inhibition of PCR. A summary table of all PCR primers used in this study is included in the supplemental material (see Data Set S1).

The quantity of target DNA in unknown samples was calculated based on a standard curve generated using known quantities of template DNA. Standards for qPCR were prepared by PCR amplification of genes from positive controls, followed by ligation into either the StrataClone (Stratagene, Santa Clara, CA) or pGEM-T Easy (Promega, Madison, WI) cloning vectors and transformation into Escherichia coli JM109. Plasmids were purified using a QIAprep Spin Miniprep kit. Plasmid DNA was quantified by staining with Hoechst 33258 dye and measured on a TD-700 fluorometer (Turner Designs, Sunnyvale, CA) using calf thymus DNA as a standard. Serial dilutions of plasmid DNA were prepared and run during each qPCR to generate standard curves (r2 of >0.99). Amplification efficiency was monitored to ensure that the samples and standards amplified with similar efficiencies (amplification efficiencies were 80 to 105%, depending on the assay). Melting curves were generated and analyzed to verify that nonspecific amplification did not occur.

Clone libraries.Fragments of amoA genes from two BAC filter samples were amplified as described above for qPCR, purified, ligated into the pGEM-T Easy cloning vector, transformed into Escherichia coli JM109, and plated onto LB agar plates supplemented with 40 μg ml−1 of ampicillin. Colonies were randomly picked so that plasmids could be extracted and purified as described above. Extracted plasmids were then used as the template for nucleotide sequence analysis using M13F and M13R as sequencing primers. Bidirectional sequence information was used to produce a consensus sequence. Phylogenetic trees were generated by optimally aligning nucleotide sequences using ClustalW (23) and then inferring phylogenetic distance using the neighbor-joining method (24) using DNAMAN ver. 7 software (Lynnon Corporation, Quebec, Canada). Reference sequences were obtained from the GenBank database (25).

Data analysis.Illumina MiSeq sequence data were processed and analyzed using mothur (26). Sequences were screened for quality; sequences containing more than one mismatch in the barcoded primer, lengths less than 50 bp or more than 125 bp, and sequences with ambiguous bases or homopolymers longer than 8 bp were removed. In addition, sequences were removed if their MiSeq-defined average quality score was less than 25 in a sliding window of 15 bp (Q25 = 0.3% of incorrect base calling). Qualifying sequences were binned by sample; primers and barcodes were trimmed from the sequence reads. Sequences were aligned with the SILVA bacterial 16S rRNA database, and the UCHIME algorithm was used to detect possible chimeric sequences, which were also removed from the data set (27). Illumina sequences were clustered into operational taxonomic units (OTUs) at a cutoff of 97% sequence identity. Taxonomic information for representative sequences for each OTU was obtained using the RDP database release 9 (28). Nonmetric multidimensional scaling (nMDS) was performed on community profiles randomly trimmed down to an equal number of sequences to avoid sampling bias. The Shannon diversity index, as well as the Chao1 and abundance-based coverage estimator (ACE) richness estimates (29), was calculated from both the trimmed and the complete sets of OTU data.

Nucleotide sequence accession number.Nucleotide sequences are available through the NCBI database under BioProject accession number PRJNA261440.

RESULTS

Process performance.More than 20 individual BAC filter units were installed in 2007 at SPRWS to improve the taste and odor of the finished water, replacing the conventional dual-medium filters (anthracite coal and sand) that were previously used. The 5-year period following installation of the BAC filters resulted in almost a 90% reduction in customer complaints compared to the previous 5-year period (before installation, 120 ± 40 complaints per year; after installation, 13 ± 4 complaints per year).

Throughout the duration of this study, the abilities of four individual BAC filters (filters 6, 9, 17, and 20) to remove total organic carbon (TOC) were tracked (Fig. 1A). These BAC filters removed 5 to 15% of the TOC, with similar removal efficiencies occurring during the summer (May through September) and winter months; raw water temperatures during this study ranged from 5°C (March 2011) to 28°C (August 2011). The ammonia and nitrite/nitrate levels in the raw and finished water were also tracked (note that the method used to detect free ammonia also detected the nitrogen content in chloramine) (Fig. 1B). Ammonia levels in the raw water were typically at or below the detection limit (<0.1 mg liter−1 as N), whereas nitrite/nitrate concentrations ranged from 0.2 to 0.6 mg liter−1 as N (arithmetic mean = 0.38 mg liter−1 as N). Nitrite/nitrate levels in the finished water were not significantly higher (two-tailed t test; P = 0.5) than those in the raw water (arithmetic mean = 0.43 mg liter−1 as N). The finished water contained chloramine (3.5 ± 0.2 mg liter−1 as Cl2), which had the potential to liberate significant quantities of ammonia upon decay (15), typically >0.6 mg liter−1 as N. This chloraminated finished water is used to backwash the BAC filters, thus providing a potential source of ammonia to the BAC filters.

FIG 1
FIG 1

Pertinent water quality parameters at the SPRWS water treatment facility during the duration of this study. (A) Concentrations of total organic carbon in the water entering the BAC filters (closed circles) and the water exiting filters 6, 9, 17, and 20 (open circles). (B) Concentrations of ammonia (circles) and nitrite/nitrate (squares) in the raw water (closed symbols) and fully treated water (open symbols).

Composition and diversity of BAC filter communities.Illumina MiSeq profiles of PCR-amplified 16S rRNA gene fragments were generated to allow filter-to-filter comparisons of bacterial community composition on a specific date as well as to follow bacterial community dynamics. A total of 4.3 million high-quality DNA sequences were obtained, with the number of sequences per profile ranging from 14,923 to 115,179 (mean, 53,700; standard deviation [SD], 19,800). A total of 4,674 different operational taxonomic units (OTUs) were detected after clustering sequences into groups at a threshold of >97% sequence identity.

The bacterial communities supported by the BAC filters were surprisingly diverse throughout the year in which they were studied (Table 1). After each Illumina MiSeq profile was randomly trimmed down to a size of 14,923 sequences (i.e., so that all profiles had the same number of reads), between 675 and 940 different OTUs were detected in each profile (mean, 824 OTUs; SD, 77 OTUs), with Chao1 estimates of richness between 1,200 and 1,800 OTUs (mean, 1,480 OTUs; SD, 192 OTUs) and the ACE estimates of richness between 1,520 and 2,570 OTUs (mean, 1950 OTUs; SD, 350 OTUs). The Shannon diversity index was generally consistent throughout the calendar year and from filter to filter, ranging from 4.4 to 4.9 (mean, 4.7; SD, 0.2). Curiously, when the Illumina MiSeq profiles were analyzed without trimming to an equal number of sequences (see Data Set S5 in the supplemental material), the number of OTUs detected was typically higher than the Chao1 richness estimates for the randomly trimmed profiles. In contrast, the ACE richness estimate from the trimmed profiles generally matched the number of OTUs detected in the untrimmed profiles, suggesting that this estimating method was more accurate at predicting the richness of these Illumina MiSeq profiles. There was almost no difference in the Shannon diversity calculations between the trimmed and untrimmed profiles, suggesting that the trimmed Illumina MiSeq profiles contained sufficient sequence depth to obtain reliable estimates of the Shannon index of diversity, consistent with a prior study (30).

View this table:
TABLE 1

Number of observed OTUs, predicted OTUs (via Chao1 and ACE estimations), and Shannon index for bacterial communities in a full-scale BAC filter as measured by Illumina MiSeq analysis of PCR-amplified 16S rRNA gene fragmentsa

The bacterial community composition of the BAC filters exhibited relatively small differences in temporal variability and in filter-to-filter variability (Fig. 2). The bacterial community compositions of three of the BAC filters (filters 3, 6, and 13) were monitored on seven different occasions over 12 months (Fig. 2A to C). Each of these three BAC filters was dynamic, as each sample date exhibited a unique community profile compared to each of the other sample dates. Similarly, there were typically substantial differences among these three bacterial communities on the individual sample dates. To further address the question of filter-to-filter variability, the bacterial communities of four additional BAC filters (filters 2, 5, 17, and 20) were profiled in July 2011 (Fig. 2D); each of these filter communities was substantially different from each other on this date, except for filters 2 and 3.

FIG 2
FIG 2

Ordination plots of bacterial community structures as determined by nonmetric multidimensional scaling (nMDS) of PCR-Illumina profiles of 16S rRNA gene fragments in the BAC filters at SPRWS. (A) Bacterial community dynamics in filter 3. (B) Bacterial community dynamics in filter 6. (C) Bacterial community dynamics in filter 13. (D) Comparison of bacterial community structures in different filters on 1 July 2011; different filters are represented by a number. ●, 15 March 2011; ■, 31 May 2011; ▲, 1 July 2011; ◆, 11 August 2011; ○, 22 November 2011; □, 15 December 2011; △, 14 February 2012. The data are shown as the arithmetic means of triplicate profiles (±1 standard deviation).

In contrast, the variation of individual populations within the bacterial community was relatively minor over time and between filters (Fig. 3). The most dominant population in all samples was a Nitrospira sp., which comprised 13% to 21% of the total community. This most prominent OTU was also manually analyzed, exhibiting 98% sequence identity (62 out of 63 nucleotides) to Nitrospira japonica strain J1 (accession number NR_114396) (31). Although some of the other bacterial populations modulated to a greater extent, the quantity of the 15 most-dominant bacterial populations within the community varied by no more than 13-fold (a Variovorax sp.) over the 12-month period (Fig. 3A). There was less variation in bacterial community composition from filter to filter at any given time, as the quantity of the 15 most-dominant bacterial populations within the community varied by no more than 8-fold (also a Variovorax sp.) among 8 different filters in July 2011 (Fig. 3B).

FIG 3
FIG 3

(A) Dynamics of several of the most dominant bacterial populations in filter 3 at SPRWS in the PCR-Illumina profiles. (B) Comparison of the representative, individual bacterial populations in different filters on 1 July 2011. The PCR-Illumina data for all bacterial profiles on all dates are included in the supplemental material.

Nitrifying bacterial community analysis.The presence of Nitrospira spp., nitrite-oxidizing bacteria (NOB) (32, 33), as the most prominent member of the bacterial community (>16%) growing on these BAC filters piqued our curiosity because very few (∼0.07%) AOB (i.e., of the order Nitrosomonadales) were detected in the Illumina MiSeq profiles. Furthermore, all of the detected AOB were Nitrosospira spp.; no Nitrosococcus spp., anaerobic ammonium-oxidizing bacteria, or ammonia-oxidizing Archaea (AOA) were detected. Although it is possible that elevated levels of nitrite existed in the source water (the analytical method used by SPRWS to characterize their water cannot distinguish between nitrite and nitrate), nitrite is generally unstable in the environment, and it would be unlikely that substantial concentrations of nitrite would be maintained for an extended duration in the source water. Hence, we were surprised to find substantial quantities of NOB without somewhat comparable quantities (i.e., within an order of magnitude) of AOB and/or AOA.

To further examine the AOB and AOA in the BAC filters, qPCR was performed targeting amoA gene fragments from the known AOB and AOA. No amoA genes from the AOA were detected in the BAC filter communities. In contrast, substantial quantities of bacterial amoA genes were detected using a qPCR technique that was previously reported as specific for Nitrosomonas oligotropha-like amoA genes (i.e., it is possible that other amoA gene types were also present in our samples). When the bacterial amoA gene fragments were normalized by the total number of 16S rRNA genes in each sample (also quantified by qPCR), the quantity of amoA genes substantially exceeded the fraction of known AOB in the Illumina MiSeq profiles and more closely aligned with the fraction of Nitrospira spp. in the Illumina MiSeq profiles (Fig. 4). The arithmetic mean of the log10 values of the fraction of all AOB in the Illumina MiSeq profiles from filters 3, 6, and 13 (n = 15) was −3.2 with a standard deviation of 0.3. In contrast, the arithmetic mean of the log10 ratio of amoA to 16S rRNA genes (n = 15) detected by qPCR was −1.8, with a standard deviation of 0.5. This difference of 1.4 in log10 quantities equates to a 25-fold disparity; a two-tailed t test of these distributions confirms that the difference of these means is statistically significant (P < 0.0001).

FIG 4
FIG 4

Comparison of the relative quantities of ammonia-oxidizing and nitrite-oxidizing bacteria in three SPRWS BAC filters. The values for AOB (Illumina) and NOB (Illumina) represent the fractions of known AOB and Nitrospira sequences, respectively, found in an Illumina profile divided by the total number of sequences in that profile. The amoA/16S rRNA represents the ratio of amoA to 16S rRNA genes as measured by quantitative real-time PCR (see Data Sets S2 to S4 in the supplemental material).

Because the qPCR primers were designed to be specific for Nitrosomonas oligotropha-like amoA gene fragments (34) but no Nitrosomonas oligotropha-like 16S rRNA gene fragments were detected in the Illumina MiSeq profiles, amoA gene fragments were amplified from samples collected from filter 6 in August 2011 and December 2011, cloned, and sequenced to confirm that the amplified amoA gene fragments were genuine. A total of 34 clones were sequenced, generating two unique DNA sequences, one of which was detected 31 times and the other of which was found on three occasions. Both of the cloned sequences closely clustered with amoA genes identified from well-characterized AOB isolates (Fig. 5). The more prominent of these clones clustered most closely with Nitrosomonas sp. strain Is79A3, whereas the less prominent clone clustered reasonably near Nitrosomonas oligotropha.

FIG 5
FIG 5

Dendrogram showing the phylogenetic relatedness of amoA gene fragment sequences (164 nucleotides) obtained in this study compared to amoA gene sequences from known ammonia-oxidizing bacteria. GenBank accession numbers of reference sequences are shown in parentheses.

DISCUSSION

The present study demonstrates that the full-scale BAC filters used to produce potable water at SPRWS contain highly diverse and stable bacterial communities. From an ecological perspective, this high degree of bacterial diversity should lead to more efficient nutrient processing, improved ecosystem stability, and resistance to process upsets (35–41). From a practical perspective, the application of this technology resulted in a significant reduction in taste and odor complaints from the water consumers. This is not surprising as conventional biologically active filters (i.e., without GAC medium) can be effective at removing numerous micropollutants of concern (42). Thus, combining bacterially mediated transformations with substantial sorption capacity, as with these BAC filters, should be even more attractive for micropollutant removal.

Our research also suggests that previously uncharacterized ammonia-oxidizing microorganisms are prominent in these BAC filters. The best line of evidence supporting this claim is the 25-fold disparity between the quantity of AOB in the Illumina MiSeq profiles and the quantity of “Nitrosomonas oligotropha-specific” amoA genes (34), which demonstrates that the genes capable of ammonia oxidation are present at significantly higher numbers than the organisms previously known to harbor these genes. Furthermore, because the PCR primers used in this study are highly specific for a small fraction of the known amoA gene sequences, the actual disparity is likely to be even greater than 25-fold. The relative numbers of amoA and 16S rRNA genes per cell could also affect this disparity, either positively or negatively. Previous researchers have assumed 2 amoA genes and 3.6 rrn operons per genome (34, 43) for AOB and all bacteria, respectively, although it is unclear how valid these assumptions would be for the organisms in the BAC filters analyzed here. In contrast, Baptista et al. (44) recently observed good agreement between quantifications obtained by qPCR targeting amoA genes, using PCR primers previously shown to be susceptible to nonspecific amplification (34), and fluorescent in situ hybridization (FISH) targeting the order Nitrosomonadales. In this same study, both of these methods exceeded the quantities obtained by qPCR targeting 16S rRNA genes of order Nitrosomonadales by an order of magnitude or more (32). Unfortunately, we were unable to perform FISH targeting the order Nitrosomonadales in this study.

Because all PCR-based assays could be biased toward or against specific populations of microorganisms, numerous alternative explanations were considered to reconcile the numbers of AOB in the Illumina MiSeq profiles and the quantity of amoA genes measured by qPCR. One possibility was the presence of significant quantities of AOA, which has been reported in previous studies on BAC filters (45, 46); qPCR targeting the amoA gene of known AOA, however, was negative (note that we have used this approach in pilot-scale BAC filters at the City of Minneapolis to successfully quantify amoA genes from AOA [unpublished results]). Next, the PCR targeting the V6 region of the 16S rRNA gene and/or the Illumina MiSeq analyses could have been biased against the known AOB in some way. Analysis of publically available 16S rRNA sequences from Nitrosomonas spp., however, confirmed 100% sequence identity with the PCR primers used in this study. In addition, artificially placed Nitrosomonas sequences in the unprocessed Illumina MiSeq data were subsequently identified as AOB sequences. Also, similar results were obtained for other samples collected from both pilot-scale (data not shown) and full-scale BAC filters at SPRWS targeting the V3 region of the 16S rRNA gene (see Data Sets S6 and S7 in the supplemental material), suggesting that PCR bias against AOB of the order Nitrosomonadales was unlikely because it would be highly improbable for two PCR protocols targeting different regions of the 16S rRNA gene to be similarly biased against the same phylogenetic group. Finally, there also could have been a bias against the AOB in the DNA extraction/purification step of our analysis. However, we have previously used this DNA extraction procedure in combination with the PCR primers targeting the V3 region of the 16S rRNA gene (absent the Illumina adapters and barcodes) to characterize a nitrifying enrichment culture in which Nitrosomonas spp. comprised as much as 25 to 70% of the bacterial community (47), again suggesting that analytical biases against the AOB of the order Nitrosomonadales are unlikely.

A second (but much weaker) line of evidence supporting the claim that novel ammonia-oxidizing organisms are present in the BAC filters is the exceptionally high ratio of NOB to AOB (NOB/AOB ratio of 225) in the Illumina MiSeq profiles. Assuming that ammonia-oxidizing microorganisms generate the nitrite that serves as the sole electron donor for the NOB and that both organisms are chemolithotrophic autotrophs, then the ratio of AOB to NOB should reflect the availability of energy from the respective metabolic reactions of these two ecological niches. Thermodynamic calculations indicate that the amount of energy available from the oxidation of ammonia to nitrite (ΔG°′ = −275 kJ mol−1) is greater than the amount of energy available from the oxidation of nitrite to nitrate (ΔG°′ = −76 kJ mol−1). In contrast, numerous previous researchers have reported that the populations of NOB are greater than the populations of AOB in microbial communities using a myriad of different methods. For example, the NOB/AOB ratios were reported to be from as low as 2.5 (21) to as high as 195 (34) using qPCR; using FISH, other researchers have reported NOB/AOB ratios ranging from 10 to 30 (48, 49). In a library of cloned, nearly complete 16S rRNA genes, this ratio was 5 from a drinking water biofilter (50). In conclusion, thermodynamic calculations suggest that AOB should outnumber NOB in bacterial communities, which contradicts empirical evidence in which the NOB are often more prominent than AOB. One alternative explanation is that the NOB are capable of outnumbering the AOB by assimilating simple organic compounds via a mixotrophic lifestyle, which was recently suggested by the genome of a Nitrospira sp. (33). Regardless, additional research is needed to better understand the relationships and roles of AOB and NOB in complex bacterial communities.

The predominance of Nitrospira spp. in the Illumina community profiles and the abundance of bacterial amoA genes detected by qPCR were unexpected because the source water contained negligible quantities of ammonia (Fig. 1B). We assume, therefore, that the ultimate source of ammonia driving the growth of nitrifying microorganisms in these BAC filters comes from organic nitrogen (which was not measured) that is converted to ammonia via ammonification during the treatment process. In addition, we speculate that the practice of backwashing with chloraminated finished water, which could liberate 0.5 to 1.0 mg/liter of ammonia upon decay, also contributes to the predominance of ammonia-oxidizing microorganisms and NOB within the bacterial community. Utilizing the backwashing flow rates (5.7 liters m−2 h−1) and frequencies (∼20 min every 7 days) and assuming typical cell yields for AOB, we calculated that the backwashing could be responsible for growing AOB that comprised as much as 0.2% of the total bacterial community (calculations not shown). This is of practical significance because the AOB in the BAC filters could provide a continuous source of nitrifying bacteria to the drinking water distribution system (51), potentially leading to nitrification events that can cause the accumulation of unacceptable levels of nitrite (52) as well as the decay of the residual chloramine that is intentionally maintained to prevent the regrowth of pathogenic organisms within the distribution system. An alternative strategy, which would potentially reduce the number of nitrifying bacteria in the BAC filters, would be to backwash these filters with filtered water prior to (i.e., rather than following) disinfection.

In conclusion, the BAC filters used at the SPRWS maintain very diverse bacterial communities that are capable of significantly improving the taste and odor of the treated water. These bacterial communities were stable over a calendar year and were dominated by a Nitrospira sp. and as-yet-unidentified ammonia-oxidizing microorganisms. The existence of previously unidentified ammonia-oxidizing microorganisms is not without precedence because cultivation is virtually a prerequisite for identifying novel ammonia-oxidizing microorganisms and most prokaryotic organisms have so far resisted isolation on microbiological media (53). Even so, our knowledge of ammonia-oxidizing microorganisms has greatly expanded over the last 2 decades to include AOB that can grow at low pH (54), anaerobic ammonium-oxidizing bacteria (55), ammonia-oxidizing Archaea (56), and even the suggestion that a single organism that can oxidize ammonia to nitrate may exist (57). Recently, other researchers have also suggested that previously unrecognized ammonia-oxidizing microorganisms were prominent in a bench-scale wastewater bioreactor performing nitrification at low dissolved-oxygen concentrations (58). The possibility that the bacterial communities analyzed here contained novel ammonia-oxidizing microorganisms was undoubtedly aided by the application of a new DNA sequencing technology (i.e., Illumina MiSeq) that permitted a much more detailed analysis of bacterial community composition, particularly of the “minor” populations that comprise less than 1% of the total community.

ACKNOWLEDGMENTS

Financial support for this work was provided by the Board of Water Commissioners of the City of Saint Paul. This work was carried out in part using computing resources at the University of Minnesota Supercomputing Institute.

We thank Jim Bode for helping coordinate this study and providing access to the facility. We also thank Daniel Noguera of the University of Wisconsin for providing clones suitable for use as a standard for qPCR targeting amoA gene fragments from AOA.

FOOTNOTES

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